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为提高运动车辆定位可靠性与精度,研究了基于交通无线传感器网络的运动车辆定位系统。根据车辆位置区域随速度变化的规律,提出了一种变区间搜索量子粒子群算法对测量的车辆定位参量进行坐标粗估计,由于噪声干扰和信号传输延时,坐标粗估计值存在一定的误差。根据车辆的运动特性引入机动目标的当前统计模型,采用扩展Kalman滤波对坐标粗估计值存在的误差进行修正,以定位速度与精度为评价指标对定位方法进行了验证。验证结果表明:无线传感网络节点可大量布设的特点提高了定位可靠性;量子粒子群中引入变区间使定位速度提高了39.13%;Kalman误差修正使得定位精度提高了56.48%。可见,本文方法可以有效提高运动车辆定位速度与准确性。
In order to improve the reliability and accuracy of moving vehicle positioning, the moving vehicle positioning system based on wireless sensor network is studied. According to the rule that the vehicle position changes with the velocity, a variable interval search quantum particle swarm algorithm is proposed to estimate the measured vehicle positioning parameters. Due to the noise interference and signal transmission delay, there is a certain error in the coarse co-ordinates estimation. The current statistical model of maneuvering target is introduced according to the vehicle motion characteristics. The error of the coarse co-ordinate estimation is corrected by extended Kalman filter, and the localization method is validated by the speed and accuracy of positioning. The verification results show that the large number of wireless sensor network nodes can improve the reliability of positioning. The variable particle interval introduces 39.13% of the speed and the Kalman error correction increases the positioning accuracy by 56.48%. Can be seen, this method can effectively improve the positioning speed and accuracy of sports vehicles.